A content delivery network (CDN), also referred to as content distribution network, is a network of distributed servers connected via IP connections, by means of which contents, in particular large media files, are delivered. CDN nodes are normally distributed to many locations and often also to many backbones. They work together for handling requests of end users, depending on the content, as comprehensively and economically as possible. In the background, the data are cached such in the network that the respective delivery is either as fast as possible (performance optimization) or needs as little bandwidth as possible (cost optimization), or both at the same time. Large CDNs keep thousands of nodes with ten thousands of servers. In principle, there are at least two kinds of CDN nodes which are defined by their functionality (content, . . . ): the CDN origin and the CDN cache. According to the prior art, the CDN origin is in a star point of the CDN network (root, hub). The CDN cache is in the area of the network (leaf, spoke), i.e. there is a root-leaf (hub & spoke) structure.
Today's Internet traffic is dominated by few large CDNs (Akamai, Limelight, Google) which distribute the content of few large providers world-wide. Moreover, large providers such as Youtube and Google also have own CDNs. For example, Netflix generates about 30% of the data traffic in North America during main traffic times. For this purpose, Netflix uses multiple CDN providers. Therefore, high-bit-rate, highly scalable and at the same time very cost efficient transport solutions for CDN are necessary, which do not exist today. More than 10% of the overall data rate of the Internet comes from Google and, according to Akamai, about 30% of the overall rate of the Internet web data are transported by Akamai. Thus, the concentration of contents increases to few CDNs. Moreover, the trend for providers of contents goes to multi CDN, i.e. the own contents are offered in parallel via multiple CDN providers.
When delivering large data capacities of content delivery network (CDN) services, the presently used delivery networks reach their limits because they are not designed for the delivery of directed high-capacity data streams which block the network, but for the decentralized delivery of many arbitrarily incoming delivery inquiries of different data capacities.
In CDN applications, very high data capacities are delivered between known locations, on the one hand for the inquiring data service and on the other hand for synchronization purposes. If these data streams are handled via conventional public networks, these high-capacity data streams block other data streams in the network between specific network points. Alternatively, the disproportionately increasing demands on DCN traffic require considerable investment in the existing IP-WDM transport network infrastructure (WDM: wavelength division multiplexing).
The only presently available scalable solution for delivering duplicates is the multicast protocol in IP. At present, there are many attempts for making the multicast protocol operable also for IPv6, in particular for the delivery of life TV (also “real-time TV” or “linear TV”). Thus, there is a need for distributing large data volumes, preferably video data.
An essential feature of CDN traffic is its high temporal and spatial dynamic. Because of very short-term optimization intervals, feed-in location and feed-in volume chance at a high frequency. IP transport networks optimize the available capacities, e.g., by means of link weights, with clearly lower frequency, often only once a day. A prerequisite for the optimization of capacities in IP transport networks are so-called “traffic matrices” by means of which the capacity requirements or capacity demands between sources and sinks in the network are determined unidirectionally and unambiguously. For the high-frequency configuration of CDNs, the concept of a quasi static traffic matrix is unsuitable; it might bring IP transport networks into an instable state by misconfiguration of the link weights.
An additional requirement of future networks is the delivery of “multipoint-to-point”, i.e. contents for a customer are supplied from spatially or logically distributed sources simultaneously/synchronously. Exemplary applications are the fixed network, mobile networks, hybrid networks or wireless networks.
Current predictions as to the data traffic to be expected in the future point to a traffic which is further growing exponentially and which is feed disproportionally by CDNs.
CDN applications make increased demands on the traffic to transport networks in view of latency, capacity, throughput and data loss. Especially the video distribution has particular demands in view of latency and bandwidth. The delivery via today's Internet (best effort) does not offer any delivery reliability. Typically, a high-bit-rate real-time delivery is possible only via short distances. The below Table 1, taken from reference [1], clarifies the relation between delivery distance (distance (server to user) in miles) and delivery time (network RTT, round trip time; 4 GB DVD download time), packet loss (typical packet loss) and data throughput (throughput).
TABLE 1Relation between distance, throughput anddownload time in a CDN network (see [1])Typical4 GB DVDDistanceNetworkPacketDownload(Server to User)RTTLossThroughputTimeLocal: <100 mi.1.6 ms 0.6%44 Mbps12min.(high qualityHDTV)Regional:16 ms0.7%4 Mbps2.2hrs.500-1,000 mi.(basicHDTV)Cross-conti-48 ms1.0%1 Mbps (SD8.2hrs.nent: ~3,000 mi.TV)Multi-conti-96 ms1.4%0.4 Mbps20hrsnent: ~6,000 mi.(poor)